M. S. Mekala

Work place: School of Communication Engineering, Yeungnam University, Republic of Korea, Gyeongsan-38541, Korea

E-mail: msmekala@yu.ac.kr


Research Interests: Soft Computing, Cloud Computing, Machine Learning, Wireless Networks


Mahammad Shareef Mekala is a post-Doctoral researcher in School of Communication Engineering, Yeungnam University, Republic of Korea. He received his B.Tech. (CSE) degree in 2011 and his M.Tech. (CSE) Degree in 2013 from JNTU University, received Ph.D. from VIT University, Vellore, India. He had also received a VIT research award in 2017 and C. V. Raman research award in 2019. His research interests include IoT, cloud computing, wireless networks, cyber-physical systems, soft computing, machine learning.

Author Articles
Classification of HHO-based Machine Learning Techniques for Clone Attack Detection in WSN

By Ramesh Vatambeti Vijay Kumar Damera Karthikeyan H. Manohar M. Sharon Roji Priya C. M. S. Mekala

DOI: https://doi.org/10.5815/ijcnis.2023.06.01, Pub. Date: 8 Dec. 2023

Thanks to recent technological advancements, low-cost sensors with dispensation and communication capabilities are now feasible. As an example, a Wireless Sensor Network (WSN) is a network in which the nodes are mobile computers that exchange data with one another over wireless connections rather than relying on a central server. These inexpensive sensor nodes are particularly vulnerable to a clone node or replication assault because of their limited processing power, memory, battery life, and absence of tamper-resistant hardware. Once an attacker compromises a sensor node, they can create many copies of it elsewhere in the network that share the same ID. This would give the attacker complete internal control of the network, allowing them to mimic the genuine nodes' behavior. This is why scientists are so intent on developing better clone assault detection procedures. This research proposes a machine learning based clone node detection (ML-CND) technique to identify clone nodes in wireless networks. The goal is to identify clones effectively enough to prevent cloning attacks from happening in the first place. Use a low-cost identity verification process to identify clones in specific locations as well as around the globe. Using the Optimized Extreme Learning Machine (OELM), with kernels of ELM ideally determined through the Horse Herd Metaheuristic Optimization Algorithm (HHO), this technique safeguards the network from node identity replicas. Using the node identity replicas, the most reliable transmission path may be selected. The procedure is meant to be used to retrieve data from a network node. The simulation result demonstrates the performance analysis of several factors, including sensitivity, specificity, recall, and detection.

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